Attempt1

library(tidyverse)

## -- Attaching packages ------------------------ tidyverse 1.2.1 --

## v ggplot2 3.2.1     v purrr   0.3.2
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   0.8.3     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0

## -- Conflicts --------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(ggplot2)
library(readr)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

MetroPriceCut1 <- read_csv("MetroPriceCut1.csv")

## Parsed with column specification:
## cols(
##   Date = col_character(),
##   PriceCut = col_double(),
##   RegionID = col_double(),
##   RegionName = col_character(),
##   SizeRank = col_double()
## )

StatePriceCut1 <- read_csv("StatePriceCut1.csv")

## Parsed with column specification:
## cols(
##   Date = col_character(),
##   PriceCut = col_double(),
##   RegionID = col_double(),
##   RegionName = col_character(),
##   SizeRank = col_double()
## )

TopTierPC      <- read_csv("PriceCutTopTier.csv")

## Parsed with column specification:
## cols(
##   Date = col_character(),
##   PriceCut = col_double(),
##   RegionID = col_double(),
##   RegionName = col_character(),
##   SizeRank = col_double()
## )

MiddleTierPC   <- read_csv("PriceCutMiddleTier.csv")

## Parsed with column specification:
## cols(
##   Date = col_character(),
##   PriceCut = col_double(),
##   RegionID = col_double(),
##   RegionName = col_character(),
##   SizeRank = col_double()
## )

BottomTierPC   <- read_csv("PriceCutBottomTier.csv")

## Parsed with column specification:
## cols(
##   Date = col_character(),
##   PriceCut = col_double(),
##   RegionID = col_double(),
##   RegionName = col_character(),
##   SizeRank = col_double()
## )

g1 <- ggplot(MetroPriceCut1, aes(Date, PriceCut)) +
  geom_point(alpha = 0.1) +
  theme_minimal()

ggplotly(g1)

g2 <- ggplot(StatePriceCut1, aes(Date, PriceCut,
                                 color = RegionName)) +
  geom_point(alpha = 0.5) +
  theme_minimal()

ggplotly(g2)

g3 <- 
ggplot(TopTierPC, aes(Date, PriceCut,
                                 color = RegionName)) +
  geom_point(alpha = 0.2) +
  theme_minimal() +
  theme(legend.position = "none")

g4 <- ggplot(MiddleTierPC, aes(Date, PriceCut,
                                 color = RegionName)) +
  geom_point(alpha = 0.2) +
  theme_minimal() +
  theme(legend.position = "none")

g5 <- ggplot(BottomTierPC, aes(Date, PriceCut,
                                 color = RegionName)) +
  geom_point(alpha = 0.2) +
  theme_minimal() +
  theme(legend.position = "none")

ggplotly(g3)

ggplotly(g4)

ggplotly(g5)